RRepoGEO

REPOGEO REPORT · LITE

NanoNets/docext

Default branch main · commit 8a08bbd5 · scanned 5/18/2026, 1:36:45 AM

GitHub: 2,019 stars · 144 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
40 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 warn · 0 fail
Objective metadata checks
AI knows your name
3 / 3
Direct prompts that named your repo
HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface NanoNets/docext, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highreadme#1
    Update README tagline to emphasize 'OCR-free' and 'developer toolkit'

    Why:

    CURRENT
    <p align="center"><em>An on-premises document information extraction and benchmarking toolkit.</em></p>
    COPY-PASTE FIX
    <p align="center"><em>An on-premises, <b>OCR-free</b> unstructured data extraction, markdown conversion, and benchmarking <b>developer toolkit</b>.</em></p>
  • mediumreadme#2
    Add a 'Why docext?' section highlighting OCR-free and on-premises benefits

    Why:

    COPY-PASTE FIX
    Add a new section, perhaps after 'Overview', titled 'Why docext?' or 'Key Differentiators'. This section should explain the benefits of its OCR-free, VLM-powered, on-premises approach compared to traditional OCR or cloud services, including points like: 'Unlike traditional OCR, docext uses advanced Vision-Language Models (VLMs) for semantic understanding, avoiding common OCR errors and limitations.' and 'Designed for on-premises deployment, docext ensures data privacy and compliance, making it ideal for sensitive document processing where cloud solutions are not an option.'
  • lowabout#3
    Refine 'About' description to explicitly mention 'developer toolkit'

    Why:

    CURRENT
    An on-premises, OCR-free unstructured data extraction, markdown conversion and benchmarking toolkit. (https://idp-leaderboard.org/)
    COPY-PASTE FIX
    An on-premises, OCR-free unstructured data extraction, markdown conversion and benchmarking <b>developer toolkit</b>. (https://idp-leaderboard.org/)

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall
0 / 2
0% of queries surface NanoNets/docext
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Google Cloud Document AI
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Google Cloud Document AI · recommended 2×
  2. UiPath Document Understanding · recommended 1×
  3. ABBYY Vantage · recommended 1×
  4. Microsoft Azure Form Recognizer · recommended 1×
  5. OpenText Intelligent Capture · recommended 1×
  • CATEGORY QUERY
    How to extract unstructured data from documents on-premises without traditional OCR?
    you: not recommended
    AI recommended (in order):
    1. UiPath Document Understanding
    2. ABBYY Vantage
    3. Microsoft Azure Form Recognizer
    4. Google Cloud Document AI
    5. OpenText Intelligent Capture
    6. Kofax TotalAgility
    7. spaCy
    8. NLTK
    9. Apache OpenNLP

    AI recommended 9 alternatives but never named NanoNets/docext. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools convert PDF and image documents into semantically tagged markdown for information extraction?
    you: not recommended
    AI recommended (in order):
    1. Azure AI Document Intelligence
    2. AWS Textract
    3. Google Cloud Document AI
    4. LayoutParser
    5. Tesseract
    6. Google Cloud Vision API
    7. Nougat
    8. PDF.co

    AI recommended 8 alternatives but never named NanoNets/docext. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    pass

  • README presence
    pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

  • Compared to common alternatives in this category, what is the core differentiator of NanoNets/docext?
    pass
    AI named NanoNets/docext explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts NanoNets/docext in production, what risks or prerequisites should they evaluate first?
    pass
    AI named NanoNets/docext explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • In one sentence, what problem does the repo NanoNets/docext solve, and who is the primary audience?
    pass
    AI named NanoNets/docext explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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NanoNets/docext — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite